| The quijote simulations F Villaescusa-Navarro, CH Hahn, E Massara, A Banerjee, AM Delgado, ... The Astrophysical Journal Supplement Series 250 (1), 2, 2020 | 193 | 2020 |
| Learning to predict the cosmological structure formation S He, Y Li, Y Feng, S Ho, S Ravanbakhsh, W Chen, B Póczos Proceedings of the National Academy of Sciences 116 (28), 13825-13832, 2019 | 178 | 2019 |
| CosmoFlow: Using deep learning to learn the universe at scale A Mathuriya, D Bard, P Mendygral, L Meadows, J Arnemann, L Shao, ... SC18: International Conference for High Performance Computing, Networking …, 2018 | 116 | 2018 |
| Constraining gravity at the largest scales through CMB lensing and galaxy velocities AR Pullen, S Alam, S He, S Ho Monthly Notices of the Royal Astronomical Society 460 (4), 4098-4108, 2016 | 82 | 2016 |
| Detecting galaxy–filament alignments in the Sloan Digital Sky Survey III YC Chen, S Ho, J Blazek, S He, R Mandelbaum, P Melchior, S Singh Monthly Notices of the Royal Astronomical Society 485 (2), 2492-2504, 2019 | 34 | 2019 |
| The detection of the imprint of filaments on cosmic microwave background lensing S He, S Alam, S Ferraro, YC Chen, S Ho Nature Astronomy 2 (5), 401-406, 2018 | 25 | 2018 |
| From dark matter to galaxies with convolutional networks X Zhang, Y Wang, W Zhang, Y Sun, S He, G Contardo, ... arXiv preprint arXiv:1902.05965, 2019 | 15 | 2019 |
| Learning neutrino effects in cosmology with convolutional neural networks E Giusarma, MR Hurtado, F Villaescusa-Navarro, S He, S Ho, CH Hahn arXiv preprint arXiv:1910.04255, 2019 | 9 | 2019 |
| Higan: Cosmic neutral hydrogen with generative adversarial networks J Zamudio-Fernandez, A Okan, F Villaescusa-Navarro, S Bilaloglu, ... arXiv preprint arXiv:1904.12846, 2019 | 9 | 2019 |
| Analysis of cosmic microwave background with deep learning S He, S Ravanbakhsh, S Ho | 7 | 2018 |
| From Dark Matter to Galaxies with Convolutional Neural Networks JHT Yip, X Zhang, Y Wang, W Zhang, Y Sun, G Contardo, ... arXiv preprint arXiv:1910.07813, 2019 | 5 | 2019 |
| From dark matter to galaxies with convolutional networks. arXiv e-prints, art X Zhang, Y Wang, W Zhang, Y Sun, S He, G Contardo, ... arXiv preprint arXiv:1902.05965, 2019 | 5 | 2019 |
| From Dark Matter to Galaxies with Convolutional Networks. arXiv e-prints X Zhang, Y Wang, W Zhang, Y Sun, S He, G Contardo, ... arXiv preprint arXiv:1902.05965, 2019 | 5 | 2019 |
| Prabhat, and V A Mathuriya, D Bard, P Mendygral, L Meadows, J Arnemann, L Shao, ... Lee,“CosmoFlow: Using Deep Learning to Learn the Universe at Scale,” ArXiv e …, 2018 | 5 | 2018 |
| Learning neutrino effects in Cosmology with Convolutional Neural Networks. arXiv e-prints E Giusarma, M Reyes Hurtado, F Villaescusa-Navarro, S He, S Ho, ... arXiv preprint arXiv:1910.04255, 2019 | 4 | 2019 |
| Simple lessons from complex learning: what a neural network model learns about cosmic structure formation D Jamieson, Y Li, S He, F Villaescusa-Navarro, S Ho, RA de Oliveira, ... arXiv preprint arXiv:2206.04573, 2022 | | 2022 |
| Innovative Techniques to Address the Non-linearity of the Large Scale Structure of the Universe S He Carnegie Mellon University, 2019 | | 2019 |
| Predicting Cosmological Massive Neutrino Simulation with Convolutional Neural Networks E Giusarma, MR Hurtado, F Villaescusa-Navarro, S He, S Ho | | |
| HIGAN: Cosmic Neutral Hydrogen with GANs J Zamudio-Fernandez, A Okan, F Villaescusa-Navarro, S Bilaloglu, ... | | |
